Vegas-Sánchez-Ferrero Gonzalo, Estépar José Raúl San
Applied Chest Imaging Laboratory (ACIL), Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Image Anal Mov Organ Breast Thorac Images (2018). 2018 Sep;11040:180-190. doi: 10.1007/978-3-030-00946-5_19. Epub 2018 Sep 12.
Recent studies have suggested the central role of small airway destruction in the pathogenesis of COPD leading to further parenchymal destruction. This evidence has sparked the interest in in-vivo assessment of small airway disease overall at the early onset of the disease. The parametric response mapping (PRM) technique has been proposed to distinguish gas trapping due to small airway disease from low attenuation areas due to emphysema. Despite its success, the PRM technique shows some limitations that are precluding the interpretation of its results. The density value used to assess gas trapping highly depends on acquisition parameters, such as dose and reconstruction kernel, and changes in body size, that introduce inhomogeneous photon absorption patterns. In particular, many studies using PRM employ inspiratory and expiratory images that are obtained at different dose levels. Emphysema impact in early disease may be confounded with the gas trapping due to the noise introduced by differences in the acquisition during the PRM. In this work, we propose a CT harmonization technique to remove the nuisance factors to distinguish between small airway disease and emphysema. Our results show that the measurements based on CT harmonization provide an increase in the detection of both emphysema and airway disease, resulting in a statistically significant impact of both components and a better association with lung function measures.
最近的研究表明,小气道破坏在慢性阻塞性肺疾病(COPD)的发病机制中起核心作用,进而导致进一步的实质破坏。这一证据引发了人们对在疾病早期对小气道疾病进行整体体内评估的兴趣。参数反应映射(PRM)技术已被提出,用于区分小气道疾病导致的气体潴留和肺气肿导致的低衰减区域。尽管取得了成功,但PRM技术存在一些局限性,妨碍了对其结果的解读。用于评估气体潴留的密度值高度依赖于采集参数,如剂量和重建内核,以及体型变化,这些因素会引入不均匀的光子吸收模式。特别是,许多使用PRM的研究采用了在不同剂量水平下获得的吸气和呼气图像。由于PRM采集过程中的差异所引入的噪声,早期疾病中的肺气肿影响可能与气体潴留混淆。在这项工作中,我们提出了一种CT归一化技术,以消除干扰因素,区分小气道疾病和肺气肿。我们的结果表明,基于CT归一化的测量提高了对肺气肿和气道疾病的检测能力,使两个组成部分都产生了具有统计学意义的影响,并与肺功能测量有更好的相关性。